TThe rapid proliferation of electric vehicles (EVs) necessitates advanced charging infrastructure, which is increasingly reliant on cloud-based technologies and the Internet of Things (IoT). However, these systems are vulnerable to cyberattacks that could have severe repercussions, including power grid failures. This paper addresses the security vulnerabilities inherent in the EV charging system. We propose a novel hybrid security solution tailored for cloud-based EV charging systems that integrates time-series-based regression models with classification algorithms to detect and mitigate both power consumption anomalies and network intrusions effectively. Our approach includes a comprehensive analysis to identify vulnerabilities and threats for cloud-based EV systems, a dual-model system for anomaly and intrusion detection, and a feedback-based threshold adjustment mechanism to assist overflow and anomaly identification. We provide a detailed analysis of data communication threats from a cloud perspective, design a robust security model, and evaluate various models to select the most effective ones for real-time security management. The finally decided models present excellent accuracy and practical value. Our findings contribute to enhancing the resilience of EV charging systems against cyber-physical threats, ensuring more reliable and secure operations.